{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mplfinance as mpf\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取示例数据\n",
    "pd_data = pd.read_csv(r'../service/requests/datas/000868.csv', parse_dates=['date'], index_col='date')\n",
    "pd_data=pd_data\n",
    "my_color = mpf.make_marketcolors(up='r',\n",
    "                                 down='g',\n",
    "                                 edge='inherit',\n",
    "                                 wick='inherit',\n",
    "                                 volume='inherit')\n",
    "my_style = mpf.make_mpf_style(marketcolors=my_color,\n",
    "                              figcolor='(0.82, 0.83, 0.85)',\n",
    "                              gridcolor='(0.82, 0.83, 0.85)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义各种字体\n",
    "# 标题格式，字体为中文字体，颜色为黑色，粗体，水平中心对齐\n",
    "title_font = {'fontname': 'SimHei',\n",
    "              'size': '16',\n",
    "              'color': 'black',\n",
    "              'weight': 'bold',\n",
    "              'va': 'bottom',\n",
    "              'ha': 'center'}\n",
    "# 红色数字格式（显示开盘收盘价）粗体红色24号字\n",
    "large_red_font = {'fontname': 'Arial',\n",
    "                  'size': '24',\n",
    "                  'color': 'red',\n",
    "                  'weight': 'bold',\n",
    "                  'va': 'bottom'}\n",
    "# 绿色数字格式（显示开盘收盘价）粗体绿色24号字\n",
    "large_green_font = {'fontname': 'Arial',\n",
    "                    'size': '24',\n",
    "                    'color': 'green',\n",
    "                    'weight': 'bold',\n",
    "                    'va': 'bottom'}\n",
    "# 小数字格式（显示其他价格信息）粗体红色12号字\n",
    "small_red_font = {'fontname': 'Arial',\n",
    "                  'size': '12',\n",
    "                  'color': 'red',\n",
    "                  'weight': 'bold',\n",
    "                  'va': 'bottom'}\n",
    "# 小数字格式（显示其他价格信息）粗体绿色12号字\n",
    "small_green_font = {'fontname': 'Arial',\n",
    "                    'size': '12',\n",
    "                    'color': 'green',\n",
    "                    'weight': 'bold',\n",
    "                    'va': 'bottom'}\n",
    "# 标签格式，可以显示中文，普通黑色12号字\n",
    "normal_label_font = {'fontname': 'SimHei',\n",
    "                     'size': '12',\n",
    "                     'color': 'black',\n",
    "                     'va': 'bottom',\n",
    "                     'ha': 'right'}\n",
    "# 普通文本格式，普通黑色12号字\n",
    "normal_font = {'fontname': 'Arial',\n",
    "               'size': '12',\n",
    "               'color': 'black',\n",
    "               'va': 'bottom',\n",
    "               'ha': 'left'}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class InterCandle:\n",
    "    def __init__(self, data, my_style):\n",
    "        self.pressed = False\n",
    "        self.xpress = None\n",
    "\n",
    "        # 初始化交互式K线图对象，历史数据作为唯一的参数用于初始化对象\n",
    "        self.data = data\n",
    "        self.style = my_style\n",
    "        # 设置初始化的K线图显示区间起点为0，即显示第0到第99个交易日的数据（前100个数据）\n",
    "        self.idx_start = 0\n",
    "        self.idx_range = 100\n",
    "        # 设置ax1图表中显示的均线类型\n",
    "        self.avg_type = 'ma'\n",
    "        self.indicator = 'macd'\n",
    "\n",
    "        # 初始化figure对象，在figure上建立三个Axes对象并分别设置好它们的位置和基本属性\n",
    "        self.fig = mpf.figure(style=my_style, figsize=(12, 8), facecolor=(0.82, 0.83, 0.85))\n",
    "        fig = self.fig\n",
    "        self.ax1 = fig.add_axes([0.08, 0.25, 0.88, 0.60])\n",
    "        self.ax2 = fig.add_axes([0.08, 0.15, 0.88, 0.10], sharex=self.ax1)\n",
    "        self.ax2.set_ylabel('volume')\n",
    "        self.ax3 = fig.add_axes([0.08, 0.05, 0.88, 0.10], sharex=self.ax1)\n",
    "        self.ax3.set_ylabel('macd')\n",
    "\n",
    "        last_data=data.iloc[-1]\n",
    "        # 初始化figure对象，在figure上预先放置文本并设置格式，文本内容根据需要显示的数据实时更新\n",
    "        self.t1 = fig.text(0.50, 0.94, '513100.SH - :', **title_font)\n",
    "        self.t2 = fig.text(0.12, 0.90, '开盘价/收盘价: ', **normal_label_font)\n",
    "        self.t3 = fig.text(0.14, 0.89, f'{np.round(last_data[\"open\"], 3)} / {np.round(last_data[\"close\"], 3)}',\n",
    "                      **large_red_font)\n",
    "        self.t4 = fig.text(0.14, 0.86, f'{last_data[\"涨跌\"]}', **small_red_font)\n",
    "        self.t5 = fig.text(0.22, 0.86, f'[{np.round(last_data[\"pctChg\"], 2)}%]', **small_red_font)\n",
    "        self.t6 = fig.text(0.12, 0.86, f'{last_data.name.date()}', **normal_label_font)\n",
    "        self.t7 = fig.text(0.40, 0.90, '最高价: ', **normal_label_font)\n",
    "        self.t8 = fig.text(0.40, 0.90, f'{last_data[\"high\"]}', **small_red_font)\n",
    "        self.t9 = fig.text(0.40, 0.86, '最低价: ', **normal_label_font)\n",
    "        self.t10 = fig.text(0.40, 0.86, f'{last_data[\"low\"]}', **small_green_font)\n",
    "        self.t11 = fig.text(0.55, 0.90, '量(万手) ', **normal_label_font)\n",
    "        self.t12 = fig.text(0.55, 0.90, f'{np.round(last_data[\"volume\"] / 10000, 3)}', **normal_font)\n",
    "\n",
    "        fig.canvas.mpl_connect('button_press_event', self.on_press)\n",
    "        fig.canvas.mpl_connect('button_release_event', self.on_release)\n",
    "        fig.canvas.mpl_connect('motion_notify_event', self.on_motion)\n",
    "        fig.canvas.mpl_connect('key_press_event', self.on_key_press)\n",
    "        fig.canvas.mpl_connect('scroll_event', self.on_scroll)\n",
    "\n",
    "    def refresh_plot(self, idx_start, idx_range):\n",
    "        \"\"\" 根据最新的参数，重新绘制整个图表\n",
    "        \"\"\"\n",
    "        all_data = self.data\n",
    "        plot_data = all_data.iloc[idx_start: idx_start + idx_range]\n",
    "\n",
    "        ap = []\n",
    "        # 添加K线图重叠均线，根据均线类型添加移动均线或布林带线\n",
    "        # if self.avg_type == 'ma':\n",
    "        #     ap.append(mpf.make_addplot(plot_data[['MA5', 'MA10', 'MA20', 'MA60']], ax=self.ax1))\n",
    "        # elif self.avg_type == 'bb':\n",
    "        #     ap.append(mpf.make_addplot(plot_data[['bb-u', 'bb-m', 'bb-l']], ax=self.ax1))\n",
    "        # # 添加指标，根据指标类型添加MACD或RSI或DEMA\n",
    "        # if self.indicator == 'macd':\n",
    "        #     ap.append(mpf.make_addplot(plot_data[['macd-m', 'macd-s']], ylabel='macd', ax=self.ax3))\n",
    "        #     bar_r = np.where(plot_data['macd-h'] > 0, plot_data['macd-h'], 0)\n",
    "        #     bar_g = np.where(plot_data['macd-h'] <= 0, plot_data['macd-h'], 0)\n",
    "        #     ap.append(mpf.make_addplot(bar_r, type='bar', color='red', ax=self.ax3))\n",
    "        #     ap.append(mpf.make_addplot(bar_g, type='bar', color='green', ax=self.ax3))\n",
    "        # elif self.indicator == 'rsi':\n",
    "        #     ap.append(mpf.make_addplot([75] * len(plot_data), color=(0.75, 0.6, 0.6), ax=self.ax3))\n",
    "        #     ap.append(mpf.make_addplot([30] * len(plot_data), color=(0.6, 0.75, 0.6), ax=self.ax3))\n",
    "        #     ap.append(mpf.make_addplot(plot_data['rsi'], ylabel='rsi', ax=self.ax3))\n",
    "        # else:  # indicator == 'dema'\n",
    "        #     ap.append(mpf.make_addplot(plot_data['dema'], ylabel='dema', ax=self.ax3))\n",
    "\n",
    "        # 绘制图表\n",
    "        mpf.plot(plot_data,\n",
    "                 ax=self.ax1,\n",
    "                 volume=self.ax2,\n",
    "                 addplot=ap,\n",
    "                 type='candle',\n",
    "                 style=self.style,\n",
    "                 datetime_format='%Y-%m',\n",
    "                 xrotation=0)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def refresh_texts(self, display_data):\n",
    "        \"\"\" 更新K线图上的价格文本\n",
    "        \"\"\"\n",
    "        # display_data是一个交易日内的所有数据，将这些数据分别填入figure对象上的文本中\n",
    "        self.t3.set_text(f'{np.round(display_data[\"open\"], 3)} / {np.round(display_data[\"close\"], 3)}')\n",
    "        self.t4.set_text(f'{np.round(display_data[\"涨跌\"], 3)}')\n",
    "        self.t5.set_text(f'[{np.round(display_data[\"pctChg\"], 3)}%]')\n",
    "        self.t6.set_text(f'{display_data.name.date()}')\n",
    "        self.t8.set_text(f'{np.round(display_data[\"high\"], 3)}')\n",
    "        self.t10.set_text(f'{np.round(display_data[\"low\"], 3)}')\n",
    "        self.t12.set_text(f'{np.round(display_data[\"volume\"] / 10000, 3)}')\n",
    "\n",
    "\n",
    "\n",
    "        # 根据本交易日的价格变动值确定开盘价、收盘价的显示颜色\n",
    "        if display_data['涨跌'] > 0:  # 如果今日变动额大于0，即今天价格高于昨天，今天价格显示为红色\n",
    "            close_number_color = 'red'\n",
    "        elif display_data['涨跌'] < 0:  # 如果今日变动额小于0，即今天价格低于昨天，今天价格显示为绿色\n",
    "            close_number_color = 'green'\n",
    "        else:\n",
    "            close_number_color = 'black'\n",
    "        self.t3.set_color(close_number_color)\n",
    "        self.t4.set_color(close_number_color)\n",
    "        self.t5.set_color(close_number_color)\n",
    "\n",
    "    def on_press(self, event):\n",
    "        if not (event.inaxes == self.ax1) and (not event.inaxes == self.ax3):\n",
    "            return\n",
    "        if event.button != 1:\n",
    "            return\n",
    "        self.pressed = True\n",
    "        self.xpress = event.xdata\n",
    "\n",
    "        # 切换当前ma类型, 在ma、bb、none之间循环\n",
    "        if event.inaxes == self.ax1 and event.dblclick == 1:\n",
    "            if self.avg_type == 'ma':\n",
    "                self.avg_type = 'bb'\n",
    "            elif self.avg_type == 'bb':\n",
    "                self.avg_type = 'none'\n",
    "            else:\n",
    "                self.avg_type = 'ma'\n",
    "        # 切换当前indicator类型，在macd/dma/rsi/kdj之间循环\n",
    "        if event.inaxes == self.ax3 and event.dblclick == 1:\n",
    "            if self.indicator == 'macd':\n",
    "                self.indicator = 'dma'\n",
    "            elif self.indicator == 'dma':\n",
    "                self.indicator = 'rsi'\n",
    "            elif self.indicator == 'rsi':\n",
    "                self.indicator = 'kdj'\n",
    "            else:\n",
    "                self.indicator = 'macd'\n",
    "\n",
    "        self.ax1.clear()\n",
    "        self.ax2.clear()\n",
    "        self.ax3.clear()\n",
    "        self.refresh_plot(self.idx_start, self.idx_range)\n",
    "\n",
    "    def on_release(self, event):\n",
    "        self.pressed = False\n",
    "        dx = int(event.xdata - self.xpress)\n",
    "        self.idx_start -= dx\n",
    "        if self.idx_start <= 0:\n",
    "            self.idx_start = 0\n",
    "        if self.idx_start >= len(self.data) - 100:\n",
    "            self.idx_start = len(self.data) - 100\n",
    "\n",
    "    def on_motion(self, event):\n",
    "        if not self.pressed:\n",
    "            return\n",
    "        if not event.inaxes == self.ax1:\n",
    "            return\n",
    "        dx = int(event.xdata - self.xpress)\n",
    "        new_start = self.idx_start - dx\n",
    "        # 设定平移的左右界限，如果平移后超出界限，则不再平移\n",
    "        if new_start <= 0:\n",
    "            new_start = 0\n",
    "        if new_start >= len(self.data) - 100:\n",
    "            new_start = len(self.data) - 100\n",
    "        self.ax1.clear()\n",
    "        self.ax2.clear()\n",
    "        self.ax3.clear()\n",
    "\n",
    "        self.refresh_texts(self.data.iloc[new_start])\n",
    "        self.refresh_plot(new_start, self.idx_range)\n",
    "\n",
    "    def on_scroll(self, event):\n",
    "        # 仅当鼠标滚轮在axes1范围内滚动时起作用\n",
    "        scale_factor = 1.0\n",
    "        if event.inaxes != self.ax1:\n",
    "            return\n",
    "        if event.button == 'down':\n",
    "            # 缩小20%显示范围\n",
    "            scale_factor = 0.8\n",
    "        if event.button == 'up':\n",
    "            # 放大20%显示范围\n",
    "            scale_factor = 1.2\n",
    "        # 设置K线的显示范围大小\n",
    "        self.idx_range = int(self.idx_range * scale_factor)\n",
    "        # 限定可以显示的K线图的范围，最少不能少于30个交易日，最大不能超过当前位置与\n",
    "        # K线数据总长度的差\n",
    "        data_length = len(self.data)\n",
    "        if self.idx_range >= data_length - self.idx_start:\n",
    "            self.idx_range = data_length - self.idx_start\n",
    "        if self.idx_range <= 30:\n",
    "            self.idx_range = 30\n",
    "            # 更新图表（注意因为多了一个参数idx_range，refresh_plot函数也有所改动）\n",
    "        self.ax1.clear()\n",
    "        self.ax2.clear()\n",
    "        self.ax3.clear()\n",
    "        self.refresh_texts(self.data.iloc[self.idx_start])\n",
    "        self.refresh_plot(self.idx_start, self.idx_range)\n",
    "\n",
    "    # 键盘按下处理\n",
    "    def on_key_press(self, event):\n",
    "        data_length = len(self.data)\n",
    "        if event.key == 'a':  # avg_type, 在ma,bb,none之间循环\n",
    "            if self.avg_type == 'ma':\n",
    "                self.avg_type = 'bb'\n",
    "            elif self.avg_type == 'bb':\n",
    "                self.avg_type = 'none'\n",
    "            elif self.avg_type == 'none':\n",
    "                self.avg_type = 'ma'\n",
    "        elif event.key == 'up':  # 向上，看仔细1倍\n",
    "            if self.idx_range > 30:\n",
    "                self.idx_range = self.idx_range // 2\n",
    "        elif event.key == 'down':  # 向下，看多1倍标的\n",
    "            if self.idx_range <= data_length - self.idx_start:\n",
    "                self.idx_range = self.idx_range * 2\n",
    "        elif event.key == 'left':\n",
    "            if self.idx_start > self.idx_range:\n",
    "                self.idx_start = self.idx_start - self.idx_range // 2\n",
    "        elif event.key == 'right':\n",
    "            if self.idx_start < data_length - self.idx_range:\n",
    "                self.idx_start = self.idx_start + self.idx_range // 2\n",
    "        self.ax1.clear()\n",
    "        self.ax2.clear()\n",
    "        self.ax3.clear()\n",
    "        self.refresh_texts(self.data.iloc[self.idx_start])\n",
    "        self.refresh_plot(self.idx_start, self.idx_range)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Mpf_Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candle = InterCandle(pd_data, my_style)\n",
    "candle.idx_start = 150\n",
    "candle.idx_range = 100\n",
    "candle.refresh_texts(pd_data.iloc[249])\n",
    "candle.refresh_plot(150, 100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}